PLANT  (SUBSYSTEM)  STATE  CONTROL
AT  INCOMPLETE  MEASUREMENT  INFORMATION
ON  THE  PARAMETER  SET  DETERMINING  ITS  DYNAMICS.
I. STRUCTURE  ANALYSIS  OF  A  NEURAL  NETWORK  ADAPTED
TO  THE  ANALYZED  INFORMATION  DYNAMICS

G. F. Malykhina, A. V. Merkusheva

Saint-Petersburg


  The limitedness of the signal local-stationarity concept in information measurement and information control systems (ICS) reflecting the state of the controlled plant (subsystem) demands the use of more perfect analysis and processing methods,  time-frequency transformations and algorithms for neural networks (NN). Significant problems occur when the plant (subsystem) state control is implemented in conditions where some parameters have no effect on the measuring system sensors, i.e. in conditions of incomplete information. The solution of this problem is obtained based on the analysis of plant-ICS system dynamics equations (in the state parameter space) and on the use of temporal NN algorithms.  The first (of three) paper parts  discusses the NN structure and learning algorithms that may adequately represent the data and controlled process dynamics.  NN structures are analyzed on the basis of a neural filter concept and learning -- on the basis of a time-dependent back-propagation algorithm.